Sports Trading Card Agent

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and successfully discloses behavioral traits like partial name matching ('Partial names work') and specific stat categories covered (AVG, HR, RBI, etc.). However, it lacks critical operational details such as error handling (what happens if the player isn't found?) or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a clear Args/Returns structure that frontloads the core purpose before diving into parameter specifics. While slightly formal/Python-docstring in style, every sentence adds value (stat categories listed, examples provided, return value summarized) with minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter lookup tool that has an output schema, the description provides appropriate coverage: it documents both parameters with examples despite empty schema descriptions, and summarizes the return structure without needing to duplicate the full output schema. It appropriately bridges the stats and card domains given the sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given the schema has 0% description coverage, the description effectively compensates by providing rich examples for 'player_name' ('Shohei Ohtani', 'Aaron Judge') and noting that partial names work, which is crucial behavioral context. It also clarifies 'season' takes year values like 2025 and references the default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with specific verbs ('Look up') and resources ('MLB player stats'), and explicitly distinguishes from siblings like 'nfl_stats_lookup' and generic 'player_stats_lookup' by specifying 'MLB'. It also clarifies the card-market connection that links it to card-related siblings without overlapping entirely.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

While the MLB specificity implies baseball contexts, there are no explicit guidelines on when to use this versus 'card_market_analysis' or 'player_stats_lookup'. The description notes the tool provides card insights but doesn't clarify if this is the right tool for pure card pricing versus 'card_price_lookup'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.